class_docstr: Block that creates a classification head.
code: def __init__(
      self,
      norm_type,
      kernel_regularizer,
      channels_per_block=(256, 512, 1024),
      num_classes=1000,
      name="classification_head",
  ):
      super(ClassificationHead, self).__init__(name=name)
      self._encoder = []
      self._logits = []
      for i in range(len(channels_per_block)):
          self._encoder.append(
              network_cb.ResidualBlock(
                  channels_per_block[i],
                  norm_type,
                  kernel_regularizer,
                  skip=True,
                  stride=2,
              )
          )
      self._logits = tf.keras.layers.Dense(
          num_classes,
          kernel_initializer="zeros",
          kernel_regularizer=kernel_regularizer(),
      )
  
imports: import gin
  import tensorflow
  from tensorflow_graphics.projects.points_to_3Dobjects.networks import networks
  import tensorflow_graphics.projects.points_to_3Dobjects.networks.custom_blocks
label_desc: Use image node_id 1 to create a new ClassificationHead object from inherited base classes: tf.keras.layers with example: obj = ClassificationHead(norm_type, kernel_regularizer, channels_per_block, num_classes, name)